THIS LECTURE. How do we know if our results are any good? Results summaries: Evaluating a search engine. Making our good results usable to a user
|
|
- Logan Bridges
- 5 years ago
- Views:
Transcription
1 EVALUATION
2 Sec. 6.2 THIS LECTURE How do we know if our results are any good? Evaluating a search engine Benchmarks Precision and recall Results summaries: Making our good results usable to a user 2
3 3 EVALUATING SEARCH ENGINES
4 Sec. 8.6 MEASURES FOR A SEARCH ENGINE How fast does it index (offline) Number of documents/hour (Average document size) How fast does it search (online) Latency as a function of index size Expressiveness of query language Ability to express complex information needs Speed on complex queries Uncluttered UI Is it free? 4
5 Sec. 8.6 MEASURES FOR A SEARCH ENGINE All of the preceding criteria are measurable: we can quantify speed/size we can make expressiveness precise The key measure: user happiness What is this? Speed of response/size of index are factors But blindingly fast, useless answers won t make a user happy Need a way of quantifying user happiness 5
6 Sec MEASURING USER HAPPINESS Issue: who is the user we are trying to make happy? Depends on the setting Web engine: User finds what s/he wants and returns to the engine Can measure rate of return users User completes task search as a means, not end See Russell June-2007-short.pdf ecommerce site: user finds what s/he wants and buys Is it the end-user, or the ecommerce site, whose happiness we measure? Measure time to purchase, or fraction of searchers who become buyers? 6
7 Sec MEASURING USER HAPPINESS Enterprise (company/govt/academic): Care about user productivity How much time do my users save when looking for information? Many other criteria having to do with breadth of access, secure access, etc. 7
8 QUIZ: USER HAPPINESS How do we measure the happiness of web search advertisers? 8
9 Sec. 8.1 HAPPINESS: ELUSIVE TO MEASURE Most common proxy: relevance of search results But how do you measure relevance? We will detail a methodology here, then examine its issues Relevance measurement requires 3 elements: 1. A benchmark document collection 2. A benchmark suite of queries 3. A usually binary assessment of either Relevant or Nonrelevant for each query and each document Some work on more-than-binary, but not the standard 9
10 Sec. 8.1 EVALUATING AN IR SYSTEM Note: the information need is translated into a query Relevance is assessed relative to the information need not the query E.g., Information need: I'm looking for information on whether drinking red wine is more effective at reducing your risk of heart attacks than white wine. Query: wine red white heart attack effective Evaluate whether the doc addresses the information need, not whether it has these words 10
11 Sec. 8.2 STANDARD RELEVANCE BENCHMARKS TREC - National Institute of Standards and Technology (NIST) has run a large IR test bed for many years Reuters and other benchmark doc collections used Retrieval tasks specified sometimes as queries Human experts mark, for each query and for each doc, Relevant or Nonrelevant or at least for subset of docs that some system returned for that query 11
12 Sec. 8.3 UNRANKED RETRIEVAL EVALUATION: PRECISION AND RECALL Precision: fraction of retrieved docs that are relevant = P(relevant retrieved) Recall: fraction of relevant docs that are retrieved = P(retrieved relevant) Relevant Nonrelevant Retrieved tp fp Not Retrieved fn tn Precision P = tp/(tp + fp) Recall R = tp/(tp + fn) 12
13 Sec. 8.3 SHOULD WE INSTEAD USE THE ACCURACY MEASURE FOR EVALUATION? Given a query, an engine classifies each doc as Relevant or Nonrelevant The accuracy of an engine: the fraction of these classifications that are correct (tp + tn) / ( tp + fp + fn + tn) Accuracy is a commonly used evaluation measure in machine learning classification work 13
14 Sec. 8.3 WHY NOT JUST USE ACCURACY? How to build a % accurate search engine on a low budget. Search for: 0 matching results found. People doing information retrieval want to find something and have a certain tolerance for junk. 14
15 QUIZ: P/R AND ACCURACY Compute the Precision, Recall, and Accuracy according to the following table: Relevant Nonrelevant Retrieved Not Retrieved
16 Sec. 8.3 PRECISION/RECALL You can get high recall (but low precision) by retrieving all docs for all queries! Recall is a non-decreasing function of the number of docs retrieved In a good system, precision decreases as either the number of docs retrieved or recall increases This is not a theorem, but a result with strong empirical confirmation 16
17 Sec. 8.3 DIFFICULTIES IN USING PRECISION/RECALL Should average over large document collection/query ensembles Need human relevance assessments People aren t reliable assessors Assessments have to be binary Nuanced assessments? Heavily skewed by collection/authorship Results may not translate from one domain to another 17
18 Sec. 8.3 A COMBINED MEASURE: F Combined measure that assesses precision/recall tradeoff is F measure (weighted harmonic mean): People usually use balanced F 1 measure F 2 1 ( b = = (1 - ) b a a P R i.e., with b = 1 or a = ½ + 1) PR P + R Harmonic mean is a conservative average See CJ van Rijsbergen, Information Retrieval 18
19 Sec. 8.3 F 1 AND OTHER AVERAGES Combined Measures Minimum Maximum Arithmetic Geometric Harmonic Precision (Recall fixed at 70%) 19
20 Sec. 8.4 EVALUATING RANKED RESULTS Evaluation of ranked results: The system can return any number of results By taking various numbers of the top returned documents (levels of recall), the evaluator can produce a precision-recall curve 20
21 Sec. 8.4 A PRECISION-RECALL CURVE Precision Recall 21
22 Sec. 8.4 AVERAGING OVER QUERIES A precision-recall graph for one query isn t a very sensible thing to look at You need to average performance over a whole bunch of queries. But there s a technical issue: Precision-recall calculations place some discontinuous points on the graph How do you determine a value (interpolate) between the points? 22
23 Sec. 8.4 INTERPOLATED PRECISION Idea: If locally precision increases with increasing recall, then you should get to count that So you take the max of precisions to right of value 23
24 Sec. 8.4 EVALUATION Graphs are good, but people want summary measures! Precision at fixed retrieval level Precision-at-k: Precision of top k results Perhaps appropriate for most of web search: all people want are good matches on the first one or two results pages But: averages badly and has an arbitrary parameter of k 11-point interpolated average precision The standard measure in the early TREC competitions: you take the precision at 11 levels of recall varying from 0 to 1 by tenths of the documents, using interpolation (the value for 0 is always interpolated!), and average them Evaluates performance at all recall levels 24
25 Sec. 8.4 TYPICAL (GOOD) 11 POINT PRECISIONS SabIR/Cornell 8A1 11pt precision from TREC 8 (1999) Precision Recall 25
26 Sec. 8.4 YET MORE EVALUATION MEASURES Mean average precision (MAP) Average of the precision value obtained for the top k documents, each time a relevant doc is retrieved Avoids interpolation, use of fixed recall levels MAP for query collection is arithmetic ave. Macro-averaging: each query counts equally R-precision If we have a known (though perhaps incomplete) set of relevant documents of size Rel, then calculate precision of the top Rel docs returned Perfect system could score
27 Sec. 8.4 VARIANCE For a test collection, it is usual that a system does crummily on some information needs (e.g., MAP = 0.1) and excellently on others (e.g., MAP = 0.7) Indeed, it is usually the case that the variance in performance of the same system across queries is much greater than the variance of different systems on the same query. That is, there are easy information needs and hard ones! 27
28 28 CREATING TEST COLLECTIONS FOR IR EVALUATION
29 Sec. 8.5 TEST COLLECTIONS 29
30 Sec. 8.5 FROM DOCUMENT COLLECTIONS TO TEST COLLECTIONS Still need Test queries Relevance assessments Test queries Must be germane to docs available Best designed by domain experts Random query terms generally not a good idea Relevance assessments Human judges, time-consuming Are human panels perfect? 30
31 Sec. 8.5 KAPPA MEASURE FOR INTER-JUDGE (DIS)AGREEMENT Kappa measure Agreement measure among judges Designed for categorical judgments Corrects for chance agreement Kappa = [ P(A) P(E) ] / [ 1 P(E) ] P(A) proportion of time judges agree P(E) what agreement would be by chance Kappa = 0 for chance agreement, 1 for total agreement. 31
32 Sec. 8.5 P(A)? P(E)? KAPPA MEASURE: EXAMPLE Number of docs Judge 1 Judge Relevant Relevant 70 Nonrelevant Nonrelevant 20 Relevant Nonrelevant 10 Nonrelevant Relevant 32
33 Sec. 8.5 KAPPA EXAMPLE P(A) = 370/400 = P(nonrelevant) = ( )/800 = P(relevant) = ( )/800 = P(E) = ^ ^2 = Kappa = ( )/( ) = Kappa > 0.8 = good agreement 0.67 < Kappa < 0.8 à tentative conclusions (Carletta 96) Depends on purpose of study For >2 judges: average pairwise kappas 33
34 Sec. 8.2 TREC TREC Ad Hoc task from first 8 TRECs is standard IR task 50 detailed information needs a year Human evaluation of pooled results returned More recently other related things: Web track, HARD track A TREC query (TREC 5) <top> <num> Number: 225 <desc> Description: What is the main function of the Federal Emergency Management Agency (FEMA) and the funding level provided to meet emergencies? Also, what resources are available to FEMA such as people, equipment, facilities? </top> 34
35 Sec. 8.2 STANDARD RELEVANCE BENCHMARKS: OTHERS GOV2 Another TREC/NIST collection 25 million web pages Largest collection that is easily available But still 3 orders of magnitude smaller than what Google/Yahoo/MSN index NTCIR East Asian language and cross-language information retrieval Cross Language Evaluation Forum (CLEF) This evaluation series has concentrated on European languages and cross-language information retrieval. Many others 35
36 Sec. 8.5 IMPACT OF INTER-JUDGE AGREEMENT Impact on absolute performance measure can be significant (0.32 vs 0.39) Little impact on ranking of different systems or relative performance Suppose we want to know if algorithm A is better than algorithm B A standard information retrieval experiment will give us a reliable answer to this question. 36
37 Sec CRITIQUE OF PURE RELEVANCE Relevance vs Marginal Relevance A document can be redundant even if it is highly relevant Duplicates The same information from different sources Marginal relevance is a better measure of utility for the user. Using facts/entities as evaluation units more directly measures true relevance. But harder to create evaluation set See Carbonell reference 37
38 Sec CAN WE AVOID HUMAN JUDGMENT? No Makes experimental work hard Especially on a large scale In some very specific settings, can use proxies E.g.: for approximate vector space retrieval, we can compare the cosine distance closeness of the closest docs to those found by an approximate retrieval algorithm But once we have test collections, we can reuse them (so long as we don t overtrain too badly) 38
39 Sec EVALUATION AT LARGE SEARCH ENGINES Search engines have test collections of queries and handranked results Recall is difficult to measure on the web Search engines often use precision at top k, e.g., k = or measures that reward you more for getting rank 1 right than for getting rank 10 right. NDCG (Normalized Discounted Cumulative Gain) Search engines also use non-relevance-based measures. Clickthrough on first result Not very reliable if you look at a single clickthrough but pretty reliable in the aggregate. Studies of user behavior in the lab A/B testing 39
40 Sec A/B TESTING Purpose: Test a single innovation Prerequisite: You have a large search engine up and running. Have most users use old system Divert a small proportion of traffic (e.g., 1%) to the new system that includes the innovation Evaluate with an automatic measure like clickthrough on first result Now we can directly see if the innovation does improve user happiness. Probably the evaluation methodology that large search engines trust most In principle less powerful than doing a multivariate regression analysis, but easier to understand 40
Information Retrieval
Introduction to Information Retrieval Lecture 5: Evaluation Ruixuan Li http://idc.hust.edu.cn/~rxli/ Sec. 6.2 This lecture How do we know if our results are any good? Evaluating a search engine Benchmarks
More informationCS6322: Information Retrieval Sanda Harabagiu. Lecture 13: Evaluation
Sanda Harabagiu Lecture 13: Evaluation Sec. 6.2 This lecture How do we know if our results are any good? Evaluating a search engine Benchmarks Precision and recall Results summaries: Making our good results
More informationPart 7: Evaluation of IR Systems Francesco Ricci
Part 7: Evaluation of IR Systems Francesco Ricci Most of these slides comes from the course: Information Retrieval and Web Search, Christopher Manning and Prabhakar Raghavan 1 This lecture Sec. 6.2 p How
More informationInforma(on Retrieval
Introduc*on to Informa(on Retrieval Lecture 8: Evalua*on 1 Sec. 6.2 This lecture How do we know if our results are any good? Evalua*ng a search engine Benchmarks Precision and recall 2 EVALUATING SEARCH
More informationCSCI 5417 Information Retrieval Systems. Jim Martin!
CSCI 5417 Information Retrieval Systems Jim Martin! Lecture 7 9/13/2011 Today Review Efficient scoring schemes Approximate scoring Evaluating IR systems 1 Normal Cosine Scoring Speedups... Compute the
More informationInformation Retrieval. Lecture 7
Information Retrieval Lecture 7 Recap of the last lecture Vector space scoring Efficiency considerations Nearest neighbors and approximations This lecture Evaluating a search engine Benchmarks Precision
More informationEvaluating search engines CE-324: Modern Information Retrieval Sharif University of Technology
Evaluating search engines CE-324: Modern Information Retrieval Sharif University of Technology M. Soleymani Fall 2014 Most slides have been adapted from: Profs. Manning, Nayak & Raghavan (CS-276, Stanford)
More informationEvaluating search engines CE-324: Modern Information Retrieval Sharif University of Technology
Evaluating search engines CE-324: Modern Information Retrieval Sharif University of Technology M. Soleymani Fall 2015 Most slides have been adapted from: Profs. Manning, Nayak & Raghavan (CS-276, Stanford)
More informationInformation Retrieval
Introduction to Information Retrieval CS3245 Information Retrieval Lecture 9: IR Evaluation 9 Ch. 7 Last Time The VSM Reloaded optimized for your pleasure! Improvements to the computation and selection
More informationSearch Evaluation. Tao Yang CS293S Slides partially based on text book [CMS] [MRS]
Search Evaluation Tao Yang CS293S Slides partially based on text book [CMS] [MRS] Table of Content Search Engine Evaluation Metrics for relevancy Precision/recall F-measure MAP NDCG Difficulties in Evaluating
More informationEvaluation. David Kauchak cs160 Fall 2009 adapted from:
Evaluation David Kauchak cs160 Fall 2009 adapted from: http://www.stanford.edu/class/cs276/handouts/lecture8-evaluation.ppt Administrative How are things going? Slides Points Zipf s law IR Evaluation For
More informationInforma(on Retrieval
Introduc)on to Informa(on Retrieval CS276 Informa)on Retrieval and Web Search Pandu Nayak and Prabhakar Raghavan Lecture 8: Evalua)on Sec. 6.2 This lecture How do we know if our results are any good? Evalua)ng
More informationThis lecture. Measures for a search engine EVALUATING SEARCH ENGINES. Measuring user happiness. Measures for a search engine
Sec. 6.2 Introduc)on to Informa(on Retrieval CS276 Informa)on Retrieval and Web Search Pandu Nayak and Prabhakar Raghavan Lecture 8: Evalua)on This lecture How do we know if our results are any good? Evalua)ng
More informationInformation Retrieval
Information Retrieval ETH Zürich, Fall 2012 Thomas Hofmann LECTURE 6 EVALUATION 24.10.2012 Information Retrieval, ETHZ 2012 1 Today s Overview 1. User-Centric Evaluation 2. Evaluation via Relevance Assessment
More informationWeb Information Retrieval. Exercises Evaluation in information retrieval
Web Information Retrieval Exercises Evaluation in information retrieval Evaluating an IR system Note: information need is translated into a query Relevance is assessed relative to the information need
More informationEvaluating search engines CE-324: Modern Information Retrieval Sharif University of Technology
Evaluating search engines CE-324: Modern Information Retrieval Sharif University of Technology M. Soleymani Fall 2016 Most slides have been adapted from: Profs. Manning, Nayak & Raghavan (CS-276, Stanford)
More informationIntroduction to Information Retrieval
Introduction to Information Retrieval http://informationretrieval.org IIR 8: Evaluation & Result Summaries Hinrich Schütze Center for Information and Language Processing, University of Munich 2013-05-07
More informationOverview. Lecture 6: Evaluation. Summary: Ranked retrieval. Overview. Information Retrieval Computer Science Tripos Part II.
Overview Lecture 6: Evaluation Information Retrieval Computer Science Tripos Part II Recap/Catchup 2 Introduction Ronan Cummins 3 Unranked evaluation Natural Language and Information Processing (NLIP)
More informationInformation Retrieval
Introduction to Information Retrieval CS276 Information Retrieval and Web Search Chris Manning, Pandu Nayak and Prabhakar Raghavan Evaluation 1 Situation Thanks to your stellar performance in CS276, you
More informationInformation Retrieval
Information Retrieval Lecture 7 - Evaluation in Information Retrieval Seminar für Sprachwissenschaft International Studies in Computational Linguistics Wintersemester 2007 1/ 29 Introduction Framework
More informationInformation Retrieval. Lecture 7 - Evaluation in Information Retrieval. Introduction. Overview. Standard test collection. Wintersemester 2007
Information Retrieval Lecture 7 - Evaluation in Information Retrieval Seminar für Sprachwissenschaft International Studies in Computational Linguistics Wintersemester 2007 1 / 29 Introduction Framework
More informationEPL660: INFORMATION RETRIEVAL AND SEARCH ENGINES. Slides by Manning, Raghavan, Schutze
EPL660: INFORMATION RETRIEVAL AND SEARCH ENGINES 1 This lecture How do we know if our results are any good? Evaluating a search engine Benchmarks Precision and recall Results summaries: Making our good
More informationRetrieval Evaluation. Hongning Wang
Retrieval Evaluation Hongning Wang CS@UVa What we have learned so far Indexed corpus Crawler Ranking procedure Research attention Doc Analyzer Doc Rep (Index) Query Rep Feedback (Query) Evaluation User
More informationLecture 5: Evaluation
Lecture 5: Evaluation Information Retrieval Computer Science Tripos Part II Simone Teufel Natural Language and Information Processing (NLIP) Group Simone.Teufel@cl.cam.ac.uk Lent 2014 204 Overview 1 Recap/Catchup
More informationNatural Language Processing and Information Retrieval
Natural Language Processing and Information Retrieval Performance Evaluation Query Epansion Alessandro Moschitti Department of Computer Science and Information Engineering University of Trento Email: moschitti@disi.unitn.it
More informationInformation Retrieval and Web Search
Information Retrieval and Web Search IR Evaluation and IR Standard Text Collections Instructor: Rada Mihalcea Some slides in this section are adapted from lectures by Prof. Ray Mooney (UT) and Prof. Razvan
More informationExperiment Design and Evaluation for Information Retrieval Rishiraj Saha Roy Computer Scientist, Adobe Research Labs India
Experiment Design and Evaluation for Information Retrieval Rishiraj Saha Roy Computer Scientist, Adobe Research Labs India rroy@adobe.com 2014 Adobe Systems Incorporated. All Rights Reserved. 1 Introduction
More informationInforma(on Retrieval
Introduc*on to Informa(on Retrieval Evalua*on & Result Summaries 1 Evalua*on issues Presen*ng the results to users Efficiently extrac*ng the k- best Evalua*ng the quality of results qualita*ve evalua*on
More informationOverview of Information Retrieval and Organization. CSC 575 Intelligent Information Retrieval
Overview of Information Retrieval and Organization CSC 575 Intelligent Information Retrieval 2 How much information? Google: ~100 PB a day; 1+ million servers (est. 15-20 Exabytes stored) Wayback Machine
More informationInforma(on Retrieval
Introduc*on to Informa(on Retrieval Evalua*on & Result Summaries 1 Evalua*on issues Presen*ng the results to users Efficiently extrac*ng the k- best Evalua*ng the quality of results qualita*ve evalua*on
More informationChapter 8. Evaluating Search Engine
Chapter 8 Evaluating Search Engine Evaluation Evaluation is key to building effective and efficient search engines Measurement usually carried out in controlled laboratory experiments Online testing can
More informationInformation Retrieval
Introduction to Information Retrieval Evaluation Rank-Based Measures Binary relevance Precision@K (P@K) Mean Average Precision (MAP) Mean Reciprocal Rank (MRR) Multiple levels of relevance Normalized Discounted
More informationSearch Engines Chapter 8 Evaluating Search Engines Felix Naumann
Search Engines Chapter 8 Evaluating Search Engines 9.7.2009 Felix Naumann Evaluation 2 Evaluation is key to building effective and efficient search engines. Drives advancement of search engines When intuition
More informationEvaluation of Retrieval Systems
Evaluation of Retrieval Systems 1 Performance Criteria 1. Expressiveness of query language Can query language capture information needs? 2. Quality of search results Relevance to users information needs
More informationCSCI 599: Applications of Natural Language Processing Information Retrieval Evaluation"
CSCI 599: Applications of Natural Language Processing Information Retrieval Evaluation" All slides Addison Wesley, Donald Metzler, and Anton Leuski, 2008, 2012! Evaluation" Evaluation is key to building
More informationDD2475 Information Retrieval Lecture 5: Evaluation, Relevance Feedback, Query Expansion. Evaluation
Sec. 7.2.4! DD2475 Information Retrieval Lecture 5: Evaluation, Relevance Feedback, Query Epansion Lectures 2-4: All Tools for a Complete Information Retrieval System Lecture 2 Lecture 3 Lectures 2,4 Hedvig
More informationCS47300: Web Information Search and Management
CS47300: Web Information Search and Management Prof. Chris Clifton 27 August 2018 Material adapted from course created by Dr. Luo Si, now leading Alibaba research group 1 AD-hoc IR: Basic Process Information
More informationEvaluation Metrics. (Classifiers) CS229 Section Anand Avati
Evaluation Metrics (Classifiers) CS Section Anand Avati Topics Why? Binary classifiers Metrics Rank view Thresholding Confusion Matrix Point metrics: Accuracy, Precision, Recall / Sensitivity, Specificity,
More informationRanked Retrieval. Evaluation in IR. One option is to average the precision scores at discrete. points on the ROC curve But which points?
Ranked Retrieval One option is to average the precision scores at discrete Precision 100% 0% More junk 100% Everything points on the ROC curve But which points? Recall We want to evaluate the system, not
More informationEvaluation of Retrieval Systems
Performance Criteria Evaluation of Retrieval Systems 1 1. Expressiveness of query language Can query language capture information needs? 2. Quality of search results Relevance to users information needs
More informationEvaluation of Retrieval Systems
Performance Criteria Evaluation of Retrieval Systems. Expressiveness of query language Can query language capture information needs? 2. Quality of search results Relevance to users information needs 3.
More informationRetrieval Evaluation
Retrieval Evaluation - Reference Collections Berlin Chen Department of Computer Science & Information Engineering National Taiwan Normal University References: 1. Modern Information Retrieval, Chapter
More informationAdvanced Search Techniques for Large Scale Data Analytics Pavel Zezula and Jan Sedmidubsky Masaryk University
Advanced Search Techniques for Large Scale Data Analytics Pavel Zezula and Jan Sedmidubsky Masaryk University http://disa.fi.muni.cz The Cranfield Paradigm Retrieval Performance Evaluation Evaluation Using
More information信息检索与搜索引擎 Introduction to Information Retrieval GESC1007
信息检索与搜索引擎 Introduction to Information Retrieval GESC1007 Philippe Fournier-Viger Full professor School of Natural Sciences and Humanities philfv8@yahoo.com Spring 2019 1 Last week We have discussed: A
More informationEvaluation of Retrieval Systems
Performance Criteria Evaluation of Retrieval Systems. Expressiveness of query language Can query language capture information needs? 2. Quality of search results Relevance to users information needs 3.
More informationCourse structure & admin. CS276A Text Information Retrieval, Mining, and Exploitation. Dictionary and postings files: a fast, compact inverted index
CS76A Text Information Retrieval, Mining, and Exploitation Lecture 1 Oct 00 Course structure & admin CS76: two quarters this year: CS76A: IR, web (link alg.), (infovis, XML, PP) Website: http://cs76a.stanford.edu/
More informationSearch Engines. Informa1on Retrieval in Prac1ce. Annota1ons by Michael L. Nelson
Search Engines Informa1on Retrieval in Prac1ce Annota1ons by Michael L. Nelson All slides Addison Wesley, 2008 Evalua1on Evalua1on is key to building effec$ve and efficient search engines measurement usually
More informationInformation Retrieval. Lecture 3: Evaluation methodology
Information Retrieval Lecture 3: Evaluation methodology Computer Science Tripos Part II Lent Term 2004 Simone Teufel Natural Language and Information Processing (NLIP) Group sht25@cl.cam.ac.uk Today 2
More informationChapter III.2: Basic ranking & evaluation measures
Chapter III.2: Basic ranking & evaluation measures 1. TF-IDF and vector space model 1.1. Term frequency counting with TF-IDF 1.2. Documents and queries as vectors 2. Evaluating IR results 2.1. Evaluation
More informationMultimedia Information Retrieval
Multimedia Information Retrieval Prof Stefan Rüger Multimedia and Information Systems Knowledge Media Institute The Open University http://kmi.open.ac.uk/mmis Multimedia Information Retrieval 1. What are
More informationRanking and Learning. Table of Content. Weighted scoring for ranking Learning to rank: A simple example Learning to ranking as classification.
Table of Content anking and Learning Weighted scoring for ranking Learning to rank: A simple example Learning to ranking as classification 290 UCSB, Tao Yang, 2013 Partially based on Manning, aghavan,
More informationRyen W. White, Matthew Richardson, Mikhail Bilenko Microsoft Research Allison Heath Rice University
Ryen W. White, Matthew Richardson, Mikhail Bilenko Microsoft Research Allison Heath Rice University Users are generally loyal to one engine Even when engine switching cost is low, and even when they are
More informationPart 11: Collaborative Filtering. Francesco Ricci
Part : Collaborative Filtering Francesco Ricci Content An example of a Collaborative Filtering system: MovieLens The collaborative filtering method n Similarity of users n Methods for building the rating
More informationRecommender Systems 6CCS3WSN-7CCSMWAL
Recommender Systems 6CCS3WSN-7CCSMWAL http://insidebigdata.com/wp-content/uploads/2014/06/humorrecommender.jpg Some basic methods of recommendation Recommend popular items Collaborative Filtering Item-to-Item:
More informationPassage Retrieval and other XML-Retrieval Tasks. Andrew Trotman (Otago) Shlomo Geva (QUT)
Passage Retrieval and other XML-Retrieval Tasks Andrew Trotman (Otago) Shlomo Geva (QUT) Passage Retrieval Information Retrieval Information retrieval (IR) is the science of searching for information in
More informationModern Information Retrieval
Modern Information Retrieval Chapter 3 Retrieval Evaluation Retrieval Performance Evaluation Reference Collections CFC: The Cystic Fibrosis Collection Retrieval Evaluation, Modern Information Retrieval,
More informationIntroduction to Computational Advertising. MS&E 239 Stanford University Autumn 2010 Instructors: Andrei Broder and Vanja Josifovski
Introduction to Computational Advertising MS&E 239 Stanford University Autumn 2010 Instructors: Andrei Broder and Vanja Josifovski 1 Lecture 4: Sponsored Search (part 2) 2 Disclaimers This talk presents
More informationSearch Engines and Learning to Rank
Search Engines and Learning to Rank Joseph (Yossi) Keshet Query processor Ranker Cache Forward index Inverted index Link analyzer Indexer Parser Web graph Crawler Representations TF-IDF To get an effective
More informationEvaluating Machine-Learning Methods. Goals for the lecture
Evaluating Machine-Learning Methods Mark Craven and David Page Computer Sciences 760 Spring 2018 www.biostat.wisc.edu/~craven/cs760/ Some of the slides in these lectures have been adapted/borrowed from
More informationChapter 6 Evaluation Metrics and Evaluation
Chapter 6 Evaluation Metrics and Evaluation The area of evaluation of information retrieval and natural language processing systems is complex. It will only be touched on in this chapter. First the scientific
More informationDATA MINING INTRODUCTION TO CLASSIFICATION USING LINEAR CLASSIFIERS
DATA MINING INTRODUCTION TO CLASSIFICATION USING LINEAR CLASSIFIERS 1 Classification: Definition Given a collection of records (training set ) Each record contains a set of attributes and a class attribute
More informationAssignment No. 1. Abdurrahman Yasar. June 10, QUESTION 1
COMPUTER ENGINEERING DEPARTMENT BILKENT UNIVERSITY Assignment No. 1 Abdurrahman Yasar June 10, 2014 1 QUESTION 1 Consider the following search results for two queries Q1 and Q2 (the documents are ranked
More informationUse of Synthetic Data in Testing Administrative Records Systems
Use of Synthetic Data in Testing Administrative Records Systems K. Bradley Paxton and Thomas Hager ADI, LLC 200 Canal View Boulevard, Rochester, NY 14623 brad.paxton@adillc.net, tom.hager@adillc.net Executive
More informationCS54701: Information Retrieval
CS54701: Information Retrieval Basic Concepts 19 January 2016 Prof. Chris Clifton 1 Text Representation: Process of Indexing Remove Stopword, Stemming, Phrase Extraction etc Document Parser Extract useful
More informationChapter 9. Classification and Clustering
Chapter 9 Classification and Clustering Classification and Clustering Classification and clustering are classical pattern recognition and machine learning problems Classification, also referred to as categorization
More informationPart 11: Collaborative Filtering. Francesco Ricci
Part : Collaborative Filtering Francesco Ricci Content An example of a Collaborative Filtering system: MovieLens The collaborative filtering method n Similarity of users n Methods for building the rating
More informationModern Retrieval Evaluations. Hongning Wang
Modern Retrieval Evaluations Hongning Wang CS@UVa What we have known about IR evaluations Three key elements for IR evaluation A document collection A test suite of information needs A set of relevance
More informationEvaluation. Evaluate what? For really large amounts of data... A: Use a validation set.
Evaluate what? Evaluation Charles Sutton Data Mining and Exploration Spring 2012 Do you want to evaluate a classifier or a learning algorithm? Do you want to predict accuracy or predict which one is better?
More informationThe Security Role for Content Analysis
The Security Role for Content Analysis Jim Nisbet Founder, Tablus, Inc. November 17, 2004 About Us Tablus is a 3 year old company that delivers solutions to provide visibility to sensitive information
More informationEvaluating Classifiers
Evaluating Classifiers Reading for this topic: T. Fawcett, An introduction to ROC analysis, Sections 1-4, 7 (linked from class website) Evaluating Classifiers What we want: Classifier that best predicts
More informationBuilding Test Collections. Donna Harman National Institute of Standards and Technology
Building Test Collections Donna Harman National Institute of Standards and Technology Cranfield 2 (1962-1966) Goal: learn what makes a good indexing descriptor (4 different types tested at 3 levels of
More informationCS347. Lecture 2 April 9, Prabhakar Raghavan
CS347 Lecture 2 April 9, 2001 Prabhakar Raghavan Today s topics Inverted index storage Compressing dictionaries into memory Processing Boolean queries Optimizing term processing Skip list encoding Wild-card
More informationEvaluation Measures. Sebastian Pölsterl. April 28, Computer Aided Medical Procedures Technische Universität München
Evaluation Measures Sebastian Pölsterl Computer Aided Medical Procedures Technische Universität München April 28, 2015 Outline 1 Classification 1. Confusion Matrix 2. Receiver operating characteristics
More informationToday s topics CS347. Inverted index storage. Inverted index storage. Processing Boolean queries. Lecture 2 April 9, 2001 Prabhakar Raghavan
Today s topics CS347 Lecture 2 April 9, 2001 Prabhakar Raghavan Inverted index storage Compressing dictionaries into memory Processing Boolean queries Optimizing term processing Skip list encoding Wild-card
More informationIntroduction to Information Retrieval
Introduction to Information Retrieval http://informationretrieval.org IIR 6: Flat Clustering Hinrich Schütze Center for Information and Language Processing, University of Munich 04-06- /86 Overview Recap
More informationA Comparative Analysis of Cascade Measures for Novelty and Diversity
A Comparative Analysis of Cascade Measures for Novelty and Diversity Charles Clarke, University of Waterloo Nick Craswell, Microsoft Ian Soboroff, NIST Azin Ashkan, University of Waterloo Background Measuring
More informationCS473: Course Review CS-473. Luo Si Department of Computer Science Purdue University
CS473: CS-473 Course Review Luo Si Department of Computer Science Purdue University Basic Concepts of IR: Outline Basic Concepts of Information Retrieval: Task definition of Ad-hoc IR Terminologies and
More informationEvaluating Classifiers
Evaluating Classifiers Reading for this topic: T. Fawcett, An introduction to ROC analysis, Sections 1-4, 7 (linked from class website) Evaluating Classifiers What we want: Classifier that best predicts
More informationINF4820 Algorithms for AI and NLP. Evaluating Classifiers Clustering
INF4820 Algorithms for AI and NLP Evaluating Classifiers Clustering Murhaf Fares & Stephan Oepen Language Technology Group (LTG) September 27, 2017 Today 2 Recap Evaluation of classifiers Unsupervised
More informationInformation Retrieval CS Lecture 06. Razvan C. Bunescu School of Electrical Engineering and Computer Science
Information Retrieval CS 6900 Lecture 06 Razvan C. Bunescu School of Electrical Engineering and Computer Science bunescu@ohio.edu Boolean Retrieval vs. Ranked Retrieval Many users (professionals) prefer
More informationArtificial Intelligence. Programming Styles
Artificial Intelligence Intro to Machine Learning Programming Styles Standard CS: Explicitly program computer to do something Early AI: Derive a problem description (state) and use general algorithms to
More informationLizhe Sun. November 17, Florida State University. Ranking in Statistics and Machine Learning. Lizhe Sun. Introduction
in in Florida State University November 17, 2017 Framework in 1. our life 2. Early work: Model Examples 3. webpage Web page search modeling Data structure Data analysis with machine learning algorithms
More informationRelevance in XML Retrieval: The User Perspective
Relevance in XML Retrieval: The User Perspective Jovan Pehcevski School of CS & IT RMIT University Melbourne, Australia jovanp@cs.rmit.edu.au ABSTRACT A realistic measure of relevance is necessary for
More information1 Machine Learning System Design
Machine Learning System Design Prioritizing what to work on: Spam classification example Say you want to build a spam classifier Spam messages often have misspelled words We ll have a labeled training
More informationInformativeness for Adhoc IR Evaluation:
Informativeness for Adhoc IR Evaluation: A measure that prevents assessing individual documents Romain Deveaud 1, Véronique Moriceau 2, Josiane Mothe 3, and Eric SanJuan 1 1 LIA, Univ. Avignon, France,
More informationLearning to Rank. Tie-Yan Liu. Microsoft Research Asia CCIR 2011, Jinan,
Learning to Rank Tie-Yan Liu Microsoft Research Asia CCIR 2011, Jinan, 2011.10 History of Web Search Search engines powered by link analysis Traditional text retrieval engines 2011/10/22 Tie-Yan Liu @
More informationCCRMA MIR Workshop 2014 Evaluating Information Retrieval Systems. Leigh M. Smith Humtap Inc.
CCRMA MIR Workshop 2014 Evaluating Information Retrieval Systems Leigh M. Smith Humtap Inc. leigh@humtap.com Basic system overview Segmentation (Frames, Onsets, Beats, Bars, Chord Changes, etc) Feature
More informationNortheastern University in TREC 2009 Million Query Track
Northeastern University in TREC 2009 Million Query Track Evangelos Kanoulas, Keshi Dai, Virgil Pavlu, Stefan Savev, Javed Aslam Information Studies Department, University of Sheffield, Sheffield, UK College
More informationIntroduction to Information Retrieval
Introduction to Information Retrieval http://informationretrieval.org IIR 16: Flat Clustering Hinrich Schütze Institute for Natural Language Processing, Universität Stuttgart 2009.06.16 1/ 64 Overview
More informationFlat Clustering. Slides are mostly from Hinrich Schütze. March 27, 2017
Flat Clustering Slides are mostly from Hinrich Schütze March 7, 07 / 79 Overview Recap Clustering: Introduction 3 Clustering in IR 4 K-means 5 Evaluation 6 How many clusters? / 79 Outline Recap Clustering:
More informationComparative Analysis of Clicks and Judgments for IR Evaluation
Comparative Analysis of Clicks and Judgments for IR Evaluation Jaap Kamps 1,3 Marijn Koolen 1 Andrew Trotman 2,3 1 University of Amsterdam, The Netherlands 2 University of Otago, New Zealand 3 INitiative
More informationPart 2: Boolean Retrieval Francesco Ricci
Part 2: Boolean Retrieval Francesco Ricci Most of these slides comes from the course: Information Retrieval and Web Search, Christopher Manning and Prabhakar Raghavan Content p Term document matrix p Information
More informationCSE 7/5337: Information Retrieval and Web Search Document clustering I (IIR 16)
CSE 7/5337: Information Retrieval and Web Search Document clustering I (IIR 16) Michael Hahsler Southern Methodist University These slides are largely based on the slides by Hinrich Schütze Institute for
More informationEnterprise Miner Tutorial Notes 2 1
Enterprise Miner Tutorial Notes 2 1 ECT7110 E-Commerce Data Mining Techniques Tutorial 2 How to Join Table in Enterprise Miner e.g. we need to join the following two tables: Join1 Join 2 ID Name Gender
More informationVK Multimedia Information Systems
VK Multimedia Information Systems Mathias Lux, mlux@itec.uni-klu.ac.at This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Results Exercise 01 Exercise 02 Retrieval
More informationBenchmarks, Performance Evaluation and Contests for 3D Shape Retrieval
Benchmarks, Performance Evaluation and Contests for 3D Shape Retrieval Afzal Godil 1, Zhouhui Lian 1, Helin Dutagaci 1, Rui Fang 2, Vanamali T.P. 1, Chun Pan Cheung 1 1 National Institute of Standards
More informationIntroduction to Information Retrieval
Introduction to Information Retrieval http://informationretrieval.org IIR 6: Flat Clustering Wiltrud Kessler & Hinrich Schütze Institute for Natural Language Processing, University of Stuttgart 0-- / 83
More informationText Classification in Electronic Discovery. Disclaimer
Text in Electronic Discovery Dave Lewis, Ph.D. David D. Lewis Consulting, LLC www.daviddlewis.com Slides for lecture via Skype on February 23, 2012 in LBSC 708/INFM 718 (Seminar on E-Discovery, Univ. of
More informationNetwork Traffic Measurements and Analysis
DEIB - Politecnico di Milano Fall, 2017 Sources Hastie, Tibshirani, Friedman: The Elements of Statistical Learning James, Witten, Hastie, Tibshirani: An Introduction to Statistical Learning Andrew Ng:
More informationCS246: Mining Massive Datasets Jure Leskovec, Stanford University
CS246: Mining Massive Datasets Jure Leskovec, Stanford University http://cs246.stanford.edu [Kumar et al. 99] 2/13/2013 Jure Leskovec, Stanford CS246: Mining Massive Datasets, http://cs246.stanford.edu
More information